SECOPS
AI-summarized Cloudflare WAF drift with risk rating in GitLab MR
Detects a Cloudflare WAF ruleset change, has an LLM explain the security impact of the diff in plain language and assign a risk rating.
How it runs
The automated pipeline, trigger to output.
- TriggerSchedule: periodic WAF check
- ActionFetch current Cloudflare rulesetsCloudflare
- ActionDiff against baseline, extract changesShell
- LogicBranch: no diff -> stop
- ActionSummarize impact + assign risk ratingOpenAI
- OutputOpen GitLab MR with AI risk summaryGitLab
What it does
This workflow makes WAF diffs reviewable by non-experts. When a change is detected, it feeds the raw rule diff to an LLM that explains what the change actually does (e.g. "this now allows requests that were previously challenged"), assigns a low/medium/high risk rating, and files a GitLab MR with that analysis attached to the baseline update.
When to use it
Use it when reviewers approve WAF changes but can't always parse raw rule expressions. The AI summary turns cryptic regex and action codes into a clear impact statement, speeding up review while keeping a human in the loop.
How it works
- 1A scheduled trigger fires.
- 2A Cloudflare action fetches the current rulesets.
- 3A shell step diffs against the stored baseline and extracts the changed rules.
- 4A logic branch ends the run if there is no diff.
- 5An OpenAI action summarizes the diff's security impact and assigns a risk rating.
- 6A GitLab output opens an MR with the baseline update and the AI risk summary in the body.
Set it up
What you configure once, before turning it on.
- 1Connect CloudflareWorkers, Pages, R2, KV — the edge stack.
- 2Connect ShellRun sandboxed commands inside the workspace.
- 3Connect OpenAIModels, embeddings, files.
- 4Connect GitLabRepos, MRs, pipelines, registry.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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